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Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free
Summary
This study introduces background-focused distribution alignment (BFDA) to improve cross-domain pedestrian detection for one-stage detectors. BFDA enhances performance by prioritizing background feature alignment, overcoming foreground-background misalignment issues.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Cross-domain pedestrian detection is vital for real-world applications, transferring knowledge from data-rich to data-scarce domains.
- Existing methods often align features at the instance or image level, with one-stage detectors prioritizing speed but lacking instance-level proposals.
- Pure image-level alignment can lead to foreground-background misalignment, where source domain foreground features incorrectly match target domain background features.
Purpose of the Study:
- To develop a novel cross-domain algorithm for rapid one-stage pedestrian detectors.
- To address the foreground-background misalignment issue in image-level feature alignment.
- To enhance the performance of domain-adaptive one-stage pedestrian detectors.
Main Methods:
- Proposes Background-Focused Distribution Alignment (BFDA), a framework prioritizing background feature alignment.
- Decouples background features from image feature maps.
- Aligns background features using a novel long-short-range discriminator, minimizing foreground feature influence.
Main Results:
- BFDA significantly enhances cross-domain pedestrian detection performance for both one-stage and two-stage detectors compared to mainstream domain adaptation techniques.
- Achieves high efficiency with YOLOv5, reaching 217.4 FPS on NVIDIA Tesla V100 (640x480 resolution).
- Demonstrates a 7-12 times increase in FPS compared to existing frameworks, highlighting practical applicability.
Conclusions:
- BFDA effectively solves the foreground-background misalignment problem in image-level cross-domain alignment for pedestrian detection.
- The proposed method offers significant performance gains and computational efficiency, making it highly suitable for real-world deployment.
- The framework's focus on background features proves critical for successful domain adaptation in one-stage detectors.
